Data usage forecast of mobile network

ABSTRACT

Embodiments of the present disclosure set forth a method for forecasting data usage in a region covered by a cellular network. The method includes defining a first subregion surrounding a first point of interest in the region; and calculating a forecasted usage amount of the cellular network in the first subregion in a second time slot based on a first usage amount of the first subregion in a first time slot, a second usage amount of a second subregion geographically adjacent to the first subregion in the first time slot, a first probability associated with mobile devices in the first subregion migrating to the second subregion, and a second probability associated with mobile devices in the second subregion migrating to the first subregion.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a 371 application of InternationalApplication PCT/CN2011/076900, filed on Jul. 6, 2011 and entitled “DATAUSAGE FORECAST OF MOBILE NETWORK.” The International Application,including any appendices or attachments thereof, is incorporated byreference herein in its entirety.

TECHNICAL FIELD

The present disclosure relates generally to a data usage forecast, morespecifically to a data usage forecast of a mobile network in a specificregion.

BACKGROUND OF THE DISCLOSURE

Data usage forecast of a mobile network is important for reservingbandwidth. Through certain forecasting mechanisms, a mobile networkservice provider may allocate bandwidth accordingly.

SUMMARY

Some embodiments of the present disclosure may generally relate tomethods for forecasting data usage in a region covered by a cellularnetwork. One example method may include defining a first subregionsurrounding a first point of interest in the region; and calculating aforecasted usage amount of the cellular network in the first subregionin a second time slot based on a first usage amount of the firstsubregion in a first time slot, a second usage amount of a secondsubregion geographically adjacent to the first subregion in the firsttime slot, a first probability associated with mobile devices in thefirst subregion migrating to the second subregion, and a secondprobability associated with mobile devices in the second subregionmigrating to the first subregion.

Other embodiments of the present disclosure may generally relate tocomputer-readable media containing instructions for forecasting datausage in a region covered by a cellular network. One examplecomputer-readable media may contain instructions, which when executed bya computing device, causes the computing device to define a firstsubregion surrounding a first point of interest in the region; and tocalculate a forecasted usage amount of the cellular network in the firstsubregion in a second time slot based on a first usage amount of thefirst subregion in a first time slot, a second usage amount of a secondsubregion geographically adjacent to the first subregion in the firsttime slot, a first probability associated with mobile devices in thefirst subregion migrating to the second subregion, and a secondprobability associated with mobile devices in the second subregionmigrating to the first subregion.

Additional embodiments of the present disclosure may generally relate tocomputing devices configured to forecast data usage in a region coveredby a cellular network. One example computing device may contain aprocessing unit. The processing unit is configured to define a firstsubregion surrounding a first point of interest in the region; and tocalculate a forecasted usage amount of the cellular network in the firstsubregion in a second time slot based on a first usage amount of thefirst subregion in a first time slot, a second usage amount of a secondsubregion geographically adjacent to the first subregion in the firsttime slot, a first probability associated with mobile devices in thefirst subregion migrating to the second subregion, and a secondprobability associated with mobile devices in the second subregionmigrating to the first subregion.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features of the present disclosure will becomemore fully apparent from the following description and appended claims,taken in conjunction with the accompanying drawings. Understanding thatthese drawings depict only several embodiments in accordance with thedisclosure and are therefore not to be considered limiting of its scope,the disclosure will be described with additional specificity and detailthrough use of the accompanying drawings.

FIG. 1A illustrates an arrangement of subregions defined in a region;

FIG. 1B illustrates an arrangement of secondary subregions defined in asubregion;

FIG. 2 is a flow chart of a method for forecasting data usage in aregion covered by a cellular network;

FIG. 3 shows a block diagram illustrating a computer program productthat is arranged for forecasting data usage in a region covered by acellular network; and

FIG. 4 shows a block diagram illustrating a computing device that isarranged for forecasting data usage in a region covered by a cellularnetwork, all arranged in accordance with at least some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the spirit or scope of the subject matterpresented here. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, and designed in awide variety of different configurations, all of which are explicitlycontemplated and make part of this disclosure.

This disclosure is drawn, inter alia, to methods, apparatus, computerprograms, and systems of forecasting data usage in a region covered by acellular network.

In the disclosure, “transition probability” broadly refers to aprobability of a mobile device located in a first place in the firsttime slot migrating to a second place in the second time slot which isthe next time slot of the first time slot. The second place isgeographically adjacent to the first place.

In some embodiments, a region covered by a cellular network may havemultiple main points of interest. The region may be a city. A main pointof interest may be a place having a relatively high density of mobiledevices supported by the cellular network. The main point of interestmay include, without limitation, a business center, a residence, afactory, a transportation station, a scenery attraction, and a school.

In some embodiments, multiple subregions may be defined in a region. Asubregion may include a main point of interest. The boundary of thesubregion may correspond to the movement of the cellular networksupported mobile devices around the main point of interest. For example,the boundary of the subregion may be the foot traffic path around themain point of interest.

In some other embodiments, several smaller secondary subregions may befurther defined in a subregion. A secondary subregion may include asecondary point of interest geographically located in the main point ofinterest. The density of the mobile devices in a subregion may be morethan the density of the mobile devices in a secondary subregion definedin the subregion. For example, when the main point of interest is amall, the secondary point of interest may include, without limitation, amovie theater, a restaurant, and an outlet.

In some embodiments, the subregions and the secondary subregions aredefined first, but not all of them will be used for further processing.The selection may be based on a hierarchy way. For example, allsubregions are selected first. If there is still enough computationresource, secondary subregions of one specific subregion will beselected.

In some embodiments, the required computation resource for forecastingdata usage in the region may be based on the time units of a forecastingscenario, the total number of the forecasting scenarios, the size of thetraffic matrix of a selected subregion or secondary subregion, and thetotal number of selected subregions and secondary subregions in theregion. The time units of a forecasting scenario may equal to theproduct of the length of the forecasting scenario and the forecastingfrequency in the forecasting scenario. For example, forecasting datausage in a region from the workdays and the weekend perspectives. Givenan hourly forecasting frequency, the time units of the workdaysforecasting scenario are 120 and the time units of the weekendforecasting scenario are 48.

A traffic matrix describes the transition probabilities for a specificsubregion within a specific time unit. An element in the traffic matrixrepresents a transition probability within a specific time unit betweentwo subregions, one subregion and one secondary subregion, or twosecondary subregions. The element may be obtained through any feasibletechniques, e.g., continuous sampling. The element may also be based onhistorical data collected in the past. The size of a traffic matrix of asubregion or a secondary subregion is associated with the number of itsadjacent subregions and secondary subregions. For example, for aselected subregion which has four adjacent subregions and zero secondarysubregions, the size of its traffic matrix is (4+1)×(4+1).

The required computation resource may be described by the followingequation:Σ^(N) _(n=1)(A _(n)+1)²·Σ^(K) _(k=1) L _(k) f,where A_(n) is the number of adjacent subregions or secondary subregionsof a selected subregion, N is the total number of the selectedsubregions and the secondary subregions, L_(k) is the time units of aforecasting scenario, K is the total number of forecasting scenarios,and f is the forecasting frequency.

In the embodiments set forth above, mobile devices carried into theselected subregion are from its four adjacent subregions. Similarly,mobile devices carried out of the selected subregion move to its fouradjacent subregions. In some embodiments, the data usage amount of theselected subregion in a second time slot may be based on the data usageamount of the selected subregion in a first time slot, which is anearlier time slot of the second time slot, the data usage amount of theadjacent subregions of the selected subregions in the first time slot,and the transition probability among the selected subregion and itsadjacent subregions. The relationship may be illustrated by thefollowing equation:Q ₀(t+1)=Σ^(M) _(m=1) Q _(m)(t)P _(m0) −Q ₀(t)Σ^(M) _(n=1) P _(0n).where Q₀(t−1) is the forecasted data usage amount of a subregion “0” atthe (t+1) time slot, M is the number of subregions or secondarysubregions adjacent to the subregion “0”, Q_(m)(t) is the data usageamount of a subregion “m” at the t time slot, Q₀(t) is the data usageamount of the subregion “0” at the t time slot, P_(m0) is the transitionprobability of mobile devices migrating from the subregion “m” to thesubregion “0”, and P_(0n) is the transition probability of mobiledevices migrating from the subregion “0” to the subregion “n”.

In response to the forecasted data usage amount of the selectedsubregion in the second time slot may, the base station in the subregionor secondary subregion is configured to allocate the network bandwidth.In some embodiments, the forecasted data usage may be used to optimizethe downlink/uplink channel allocation by predicting which link willneed additional capacity. In some other embodiments, the forecasted datause may be helpful for channel allocation among neighboring cells andhandoff decisions.

FIG. 1A illustrates an arrangement of subregions defined in a regionaccording to some embodiments of the disclosure. Region 100 is a city.The region 100 includes first street 191, second street 193, firstavenue 181, and second avenue 183. Nine subregions 101, 102, 103 . . .109 are defined in region 100. Subregion 105 is a block surrounded byfirst street 191, second street 193, first avenue 181, and second avenue183. Subregion 105 may include a mall which can be entered or left fromany one of first gate 133 on first street 191, second gate 137 on secondstreet 193, third gate 131 on first avenue 181, and fourth gate 135 onsecond avenue 183.

FIG. 1B illustrates an arrangement of secondary subregions defined in asubregion according to some embodiments of the disclosure. As set forthabove, subregion 105 includes a mall. The mall includes outlet 111,movie theater 113, and restaurant 115. Gates 121, 122, 123, 124, 125,and 126 are the entrances/exits for outlet 111, movie theater 113, orrestaurant 115.

In some embodiments, when forecasting data usage in subregion 105 for aday on an hourly basis, the forecasting may include constructing 24traffic matrices for the subregion 105. Each traffic matrix representstransition probabilities within an hour in sequence. As set forth above,any one of the traffic matrix is a (4+1)×(4+1) matrix. Any one of thetraffic matrix may represent hourly transition probability between thesubregion 105, and its adjacent subregions 102, 104, 106, and 108.Elements in one matrix include transition probabilities from subregion105 to its adjacent subregions 102, 104, 106, 108, and from the adjacentsubregions 102, 104, 106, 108 to subregion 105.

The forecasting may further include checking whether the computationresource is capable of forecasting data usage in subregion 105 based onthe required condition set forth above, e.g., the number of subregionsto be forecasted, the size of the traffic matrices of the subregions tobe forecasted, the forecasting frequency, and the total duration of theforecast.

The forecasting may further include calculating a forecasted usageamount in subregion 105 in a second time slot. The forecasted usageamount may be calculated by the following equation:Q ₁₀₅(2)=Q ₁₀₂(1)P ₁₀₂₁₀₅ +Q ₁₀₆(1)P ₁₀₆₁₀₅ +Q ₁₀₈(1)P ₁₀₈₁₀₅ +Q ₁₀₄(1)P₁₀₄₁₀₅ −Q ₁₀₅(1)P ₁₀₅₁₀₂ −Q ₁₀₅(1)P ₁₀₅₁₀₆ −Q ₁₀₅(1)P ₁₀₅₁₀₈ −Q ₁₀₅(1)P₁₀₅₁₀₄.

where P₁₀₂₁₀₅, P₁₀₆₁₀₅, P₁₀₈₁₀₅, P₁₀₄₁₀₅, P₁₀₅₁₀₂, P₁₀₅₁₀₆, P₁₀₅₁₀₈,P₁₀₅₁₀₄ are obtained from the first matrix of the 24 matrices. Thetransition probabilities may be based on historical collected data.Q_(x)(1), where x=102, 104, 105, 106, and 108, represents the data usageof a specific subregion at a first time slot, which is the previous timeslot of the second time slot. Q_(x)(1) may be retrieved from the basestation. In response to the data access request from mobile devices inthe specific subregion within the first time slot, the base station maytransmit/receive data to/from the mobile devices and relevantinformation is recorded in the base station.

FIG. 2 is a flow chart of an illustrative embodiment of a method 200 forforecasting data usage in a region covered by a cellular network. Themethod 200 may include one or more operations, functions, or actionsillustrated by blocks 201 and 203. Although the blocks are illustratedin a sequential order, these blocks may also be performed in parallel,and/or in a different order than those described herein. Also, thevarious blocks may be combined into fewer blocks, divided intoadditional blocks, and/or eliminated based upon the desiredimplementation. The method 200 may begin at block 201.

At block 201, the method 200 includes defining a subregion in the regioncovered by the cellular network. In some embodiments, the subregionsurrounds a point of interest. The point of interest may be a placehaving a relatively high density of mobile devices supported by thecellular network. The boundary of the subregion may correspond to themovement of the mobile devices around the point of interest. The method200 continues at block 203.

At block 203, the method 200 includes calculating a forecasted usageamount of cellular network in the subregion defined at block 201 in asecond time slot. The calculating may be based on the usage amount ofthe subregion in a first time slot prior to the second time slot, theusage amount of a place geographically adjacent to the subregion in thefirst time slot, a first transition probability of mobile devices in thesubregion migrating to the place, and a second transition probability ofmobile devices in the place migrating to the subregion.

FIG. 3 shows a block diagram illustrating a computer program productthat is arranged for selecting a preferred data set. The computerprogram product 300 may include a signal bearing medium 304, which mayinclude one or more sets of executable instructions 302 that, whenexecuted by, for example, a processor of a computing device, may provideat least the functionality described above and illustrated in FIG. 2.

In some implementations, the signal bearing medium 304 may encompass anon-transitory computer readable medium 308, such as, but not limitedto, a hard disk drive, a Compact Disc (CD), a Digital Versatile Disk(DVD), a digital tape, memory, etc. In some implementations, the signalbearing medium 304 may encompass a recordable medium 310, such as, butnot limited to, memory, read/write (R/W) CDs, R/W DVDs, etc. In someimplementations, the signal bearing medium 304 may encompass acommunications medium 306, such as, but not limited to, a digital and/oran analog communication medium (e.g., a fiber optic cable, a waveguide,a wired communications link, a wireless communication link, etc.) Thecomputer program product 300 may also be recorded in the non-transitorycomputer readable medium 308 or another similar recordable medium 310.

FIG. 4 shows a block diagram of an illustrative embodiment of acomputing device that is arranged for forecasting data usage in a regioncovered by a cellular network. In a very basic configuration 401,computing device 400 typically includes one or more processors 410 and asystem memory 420. A memory bus 430 may be used for communicatingbetween processor 410 and system memory 420.

Depending on the desired configuration, processor 410 may be of any typeincluding but not limited to a microprocessor (μP), a microcontroller(μC), a digital signal processor (DSP), or any combination thereof.Processor 410 may include one more levels of caching, such as a levelone cache 411 and a level two cache 412, a processor core 413, andregisters 414. An example processor core 413 may include an arithmeticlogic unit (ALU), a floating point unit (FPU), a digital signalprocessing core (DSP Core), or any combination thereof. An examplememory controller 415 may also be used with processor 410, or in someimplementations memory controller 415 may be an internal part ofprocessor 410.

Depending on the desired configuration, system memory 420 may be of anytype including but not limited to volatile memory (such as RAM),non-volatile memory (such as ROM, flash memory, etc.) or any combinationthereof. System memory 420 may include an operating system 421, one ormore applications 422, and program data 424. In some embodiments,application 422 may include a data usage forecasting algorithm 423 thatis arranged to perform the functions as described herein including thosedescribed with respect to the steps 201 and 203 of the method 200 ofFIG. 2. Program data 424 may include cellular network traffic andgeographic data 425 that may be useful for the operation of data usageforecasting algorithm 423 as will be further described below. In someembodiments, the cellular network traffic and geographic data 425 mayinclude, without limitation, geographic data associated with definedsubregions and/or secondary subregions and transition probabilitiesbetween geographically adjacent two subregions, one subregion and onesecondary subregion, or two secondary subregions. In some embodiments,application 422 may be arranged to operate with program data 424 onoperating system 421 such that implementations of selecting preferreddata set may be provided as described herein. This described basicconfiguration 401 is illustrated in FIG. 4 by those components withinthe inner dashed line.

In some other embodiments, application 422 may include data usageforecasting algorithm 423 that is arranged to perform the functions asdescribed herein including those described with respect to the steps 201and 203 of the method 200 of FIG. 2.

Computing device 400 may have additional features or functionality, andadditional interfaces to facilitate communications between basicconfiguration 401 and any required devices and interfaces. For example,a bus/interface controller 440 may be used to facilitate communicationsbetween basic configuration 401 and one or more data storage devices 450via a storage interface bus 441. Data storage devices 450 may beremovable storage devices 451, non-removable storage devices 452, or acombination thereof. Examples of removable storage and non-removablestorage devices include magnetic disk devices such as flexible diskdrives and hard-disk drives (HDD), optical disk drives such as compactdisk (CD) drives or digital versatile disk (DVD) drives, solid statedrives (SSD), and tape drives to name a few. Example computer storagemedia may include volatile and nonvolatile, removable and non-removablemedia implemented in any method or technology for storage ofinformation, such as computer readable instructions, data structures,program modules, or other data.

System memory 420, removable storage devices 451 and non-removablestorage devices 452 are examples of computer storage media. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich may be used to store the desired information and which may beaccessed by computing device 400. Any such computer storage media may bepart of computing device 400.

Computing device 400 may also include an interface bus 442 forfacilitating communication from various interface devices (e.g., outputdevices 460, peripheral interfaces 470, and communication devices 480)to basic configuration 401 via bus/interface controller 440. Exampleoutput devices 460 include a graphics processing unit 461 and an audioprocessing unit 462, which may be configured to communicate to variousexternal devices such as a display or speakers via one or more A/V ports463. Example peripheral interfaces 470 include a serial interfacecontroller 471 or a parallel interface controller 472, which may beconfigured to communicate with external devices such as input devices(e.g., keyboard, mouse, pen, voice input device, touch input device,etc.) or other peripheral devices (e.g., printer, scanner, etc.) via oneor more I/O ports 473. An example communication device 480 includes anetwork controller 481, which may be arranged to facilitatecommunications with one or more other computing devices 490 over anetwork communication link via one or more communication ports 482. Insome embodiments, the other computing devices 490 may include otherapplications, which may be operated based on the results of theapplication 422.

The network communication link may be one example of a communicationmedia. Communication media may typically be embodied by computerreadable instructions, data structures, program modules, or other datain a modulated data signal, such as a carrier wave or other transportmechanism, and may include any information delivery media. A “modulateddata signal” may be a signal that has one or more of its characteristicsset or changed in such a manner as to encode information in the signal.By way of example, and not limitation, communication media may includewired media such as a wired network or direct-wired connection, andwireless media such as acoustic, radio frequency (RF), microwave,infrared (IR) and other wireless media. The term computer readable mediaas used herein may include both storage media and communication media.

Computing device 400 may be implemented as a portion of a small-formfactor portable (or mobile) electronic device such as a cell phone, apersonal data assistant (PDA), a personal media player device, awireless web-watch device, a personal headset device, an applicationspecific device, or a hybrid device that include any of the abovefunctions. Computing device 400 may also be implemented as a personalcomputer including both laptop computer and non-laptop computerconfigurations.

There is little distinction left between hardware and softwareimplementations of aspects of systems; the use of hardware or softwareis generally (but not always, in that in certain contexts the choicebetween hardware and software can become significant) a design choicerepresenting cost versus efficiency tradeoffs. There are variousvehicles by which processes and/or systems and/or other technologiesdescribed herein can be effected (e.g., hardware, software, and/orfirmware), and that the preferred vehicle will vary with the context inwhich the processes and/or systems and/or other technologies aredeployed. For example, if an implementer determines that speed andaccuracy are paramount, the implementer may opt for a mainly hardwareand/or firmware vehicle; if flexibility is paramount, the implementermay opt for a mainly software implementation; or, yet againalternatively, the implementer may opt for some combination of hardware,software, and/or firmware.

The foregoing detailed description has set forth various embodiments ofthe devices and/or processes via the use of block diagrams, flowcharts,and/or examples. Insofar as such block diagrams, flowcharts, and/orexamples contain one or more functions and/or operations, it will beunderstood by those within the art that each function and/or operationwithin such block diagrams, flowcharts, or examples can be implemented,individually and/or collectively, by a wide range of hardware, software,firmware, or virtually any combination thereof. In one embodiment,several portions of the subject matter described herein may beimplemented via Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), digital signal processors (DSPs), orother integrated formats. However, those skilled in the art willrecognize that some aspects of the embodiments disclosed herein, inwhole or in part, can be equivalently implemented in integratedcircuits, as one or more computer programs running on one or morecomputers (e.g., as one or more programs running on one or more computersystems), as one or more programs running on one or more processors(e.g., as one or more programs running on one or more microprocessors),as firmware, or as virtually any combination thereof, and that designingthe circuitry and/or writing the code for the software and or firmwarewould be well within the skill of one of skill in the art in light ofthis disclosure. In addition, those skilled in the art will appreciatethat the mechanisms of the subject matter described herein are capableof being distributed as a program product in a variety of forms, andthat an illustrative embodiment of the subject matter described hereinapplies regardless of the particular type of signal bearing medium usedto actually carry out the distribution. Examples of a signal bearingmedium include, but are not limited to, the following: a recordable typemedium such as a floppy disk, a hard disk drive, a Compact Disc (CD), aDigital Versatile Disk (DVD), a digital tape, a computer memory, etc.;and a transmission type medium such as a digital and/or an analogcommunication medium (e.g., a fiber optic cable, a waveguide, a wiredcommunications link, a wireless communication link, etc.).

Those skilled in the art will recognize that it is common within the artto describe devices and/or processes in the fashion set forth herein,and thereafter use engineering practices to integrate such describeddevices and/or processes into data processing systems. That is, at leasta portion of the devices and/or processes described herein can beintegrated into a data processing system via a reasonable amount ofexperimentation. Those having skill in the art will recognize that atypical data processing system generally includes one or more of asystem unit housing, a video display device, a memory such as volatileand non-volatile memory, processors such as microprocessors and digitalsignal processors, computational entities such as operating systems,drivers, graphical user interfaces, and applications programs, one ormore interaction devices, such as a touch pad or screen, and/or controlsystems including feedback loops and control motors (e.g., feedback forsensing position and/or velocity; control motors for moving and/oradjusting components and/or quantities). A typical data processingsystem may be implemented utilizing any suitable commercially availablecomponents, such as those typically found in datacomputing/communication and/or network computing/communication systems.

The herein described subject matter sometimes illustrates differentcomponents contained within, or connected with, different othercomponents. It is to be understood that such depicted architectures aremerely exemplary, and that in fact many other architectures can beimplemented which achieve the same functionality. In a conceptual sense,any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality can be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected”, or“operably coupled”, to each other to achieve the desired functionality,and any two components capable of being so associated can also be viewedas being “operably couplable”, to each other to achieve the desiredfunctionality. Specific examples of operably couplable include but arenot limited to physically mateable and/or physically interactingcomponents and/or wirelessly interactable and/or wirelessly interactingcomponents and/or logically interacting and/or logically interactablecomponents.

With respect to the use of substantially any plural and/or singularterms herein, those having skill in the art can translate from theplural to the singular and/or from the singular to the plural as isappropriate to the context and/or application. The varioussingular/plural permutations may be expressly set forth herein for sakeof clarity.

It will be understood by those within the art that, in general, termsused herein, and especially in the appended claims (e.g., bodies of theappended claims) are generally intended as “open” terms (e.g., the term“including” should be interpreted as “including but not limited to,” theterm “having” should be interpreted as “having at least,” the term“includes” should be interpreted as “includes but is not limited to,”etc.). It will be further understood by those within the art that if aspecific number of an introduced claim recitation is intended, such anintent will be explicitly recited in the claim, and in the absence ofsuch recitation no such intent is present. For example, as an aid tounderstanding, the following appended claims may contain usage of theintroductory phrases “at least one” and “one or more” to introduce claimrecitations. However, the use of such phrases should not be construed toimply that the introduction of a claim recitation by the indefinitearticles “a” or “an” limits any particular claim containing suchintroduced claim recitation to disclosures containing only one suchrecitation, even when the same claim includes the introductory phrases“one or more” or “at least one” and indefinite articles such as “a” or“an” (e.g., “a” and/or “an” should typically be interpreted to mean “atleast one” or “one or more”); the same holds true for the use ofdefinite articles used to introduce claim recitations. In addition, evenif a specific number of an introduced claim recitation is explicitlyrecited, those skilled in the art will recognize that such recitationshould typically be interpreted to mean at least the recited number(e.g., the bare recitation of “two recitations,” without othermodifiers, typically means at least two recitations, or two or morerecitations). Furthermore, in those instances where a conventionanalogous to “at least one of A, B, and C, etc.” is used, in generalsuch a construction is intended in the sense one having skill in the artwould understand the convention (e.g., “a system having at least one ofA, B, and C” would include but not be limited to systems that have Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). In those instances where aconvention analogous to “at least one of A, B, or C, etc.” is used, ingeneral such a construction is intended in the sense one having skill inthe art would understand the convention (e.g., “a system having at leastone of A, B, or C” would include but not be limited to systems that haveA alone, B alone, C alone, A and B together, A and C together, B and Ctogether, and/or A, B, and C together, etc.). It will be furtherunderstood by those within the art that virtually any disjunctive wordand/or phrase presenting two or more alternative terms, whether in thedescription, claims, or drawings, should be understood to contemplatethe possibilities of including one of the terms, either of the terms, orboth terms. For example, the phrase “A or B” will be understood toinclude the possibilities of “A” or “B” or “A and B.”

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

We claim:
 1. A method for forecasting data usage in a region covered bya cellular network, comprising: defining, by one or more processors of acomputing device configured to forecast data usage in the cellularnetwork, a first subregion surrounding a first point of interest in theregion; receiving, by the one or more processors, a first usage amountof the first subregion in a first time slot and a second usage amount ofa second subregion geographically adjacent to the first subregion in thefirst time slot collected by one or more base stations in the cellularnetwork; calculating, by the one or more processors, a forecasted usageamount of the cellular network in the first subregion in a second timeslot based on the first usage amount of the first subregion in the firsttime slot, the second usage amount of the second subregion in the firsttime slot, a first probability associated with mobile devices in thefirst subregion migrating to the second subregion, and a secondprobability associated with mobile devices in the second subregionmigrating to the first subregion, wherein the first time slot is anearlier time slot with respect to the second time slot; and reserving abandwidth of the cellular network in the region based on the forecastedusage amount of the cellular network.
 2. The method of claim 1, whereinthe boundary of the first subregion approximately corresponds tomovement of mobile devices around the first point of interest.
 3. Themethod of claim 1, wherein a first density of mobile devices associatedwith the first point of interest is greater than a first threshold. 4.The method of claim 3, further comprising defining a third subregion inthe first subregion, wherein the third subregion surrounds a third pointof interest having a third density of mobile devices that is greaterthan a third threshold but less than the first threshold.
 5. The methodof claim 4, further comprising determining whether to calculate theusage of the cellular network in the third subregion based on apredefined frequency factor, a predefined time slot, and the number ofsubregions defined in the region.
 6. The method of claim 1, wherein thereserving a bandwidth of the cellular network comprises reserving thebandwidth of the cellular network in the first subregion in the secondtime slot based on the forecasted usage amount of the cellular network.7. A non-transitory computer-readable storage medium encoded withcomputer-executable instructions for forecasting data usage in a regioncovered by a cellular network, which when executed by a processor of acomputing device configured to forecast data usage in the cellularnetwork, causes the computing device to: define a first subregionsurrounding a first point of interest in the region; receive a firstusage amount of the first subregion in a first time slot and a secondusage amount of a second subregion geographically adjacent to the firstsubregion in the first time slot collected by one or more base stationsin the cellular network; calculate a forecasted usage amount of thecellular network in the first subregion in a second time slot based onthe first usage amount of the first subregion in the first time slot,the second usage amount of the second subregion in the first time slot,a first probability associated with mobile devices in the firstsubregion migrating to the second subregion, and a second probabilityassociated with mobile devices in the second subregion migrating to thefirst subregion, wherein the first time slot is an earlier time slotwith respect to the second time slot; and reserve a bandwidth of thecellular network in the region based on the forecasted usage amount ofthe cellular network.
 8. The non-transitory computer-readable storagemedium of claim 7, wherein the boundary of the first subregionapproximately corresponds to movement of mobile devices around the firstpoint of interest.
 9. The non-transitory computer-readable storagemedium of claim 7, wherein a first density of mobile devices associatedwith the first point of interest is greater than a first threshold. 10.The non-transitory computer-readable storage medium of claim 9, furthercontaining additional instructions, which when executed by the computingdevice, causes the computing device to define a third subregion in thefirst subregion, wherein the third subregion surrounds a third point ofinterest having a third density of mobile devices greater than a thirdthreshold but less than the first threshold.
 11. The non-transitorycomputer-readable storage medium of claim 10, further containingadditional instructions, which when executed by the computing device,causes the computing device to determine whether to calculate the usageof the cellular network in the third subregion based on a predefinedfrequency factor, a predefined time slot, and the number of subregionsdefined in the region.
 12. The non-transitory computer-readable storagemedium of claim 7, wherein the bandwidth of the cellular network in thefirst subregion in the second time slot is reserved based on theforecasted usage amount of the cellular network.
 13. A computing deviceconfigured to forecast data usage in a region covered by a cellularnetwork, comprising: a computer storage medium; and a processing unit,wherein in response to the processing unit executing instructionsencoded in the computer storage medium, the processing unit isconfigured to: define a first subregion surrounding a first point ofinterest in the region, receive a first usage amount of the firstsubregion in a first time slot and a second usage amount of a secondsubregion geographically adjacent to the first subregion in the firsttime slot collected by one or more base stations in the cellularnetwork, calculate a forecasted usage amount of the cellular network inthe first subregion in a second time slot based on the first usageamount of the first subregion in the first time slot, the second usageamount of the second subregion in the first time slot, a firstprobability associated with mobile devices in the first subregionmigrating to the second subregion, and a second probability associatedwith mobile devices in the second subregion migrating to the firstsubregion, wherein the first time slot is an earlier time slot withrespect to the second time slot, and reserve a bandwidth of the cellularnetwork in the region based on the forecasted usage amount of thecellular network.
 14. The computing device of claim 13, wherein theboundary of the first subregion approximately corresponds to movement ofmobile devices around the first point of interest.
 15. The computingdevice of claim 13, wherein a first density of mobile devices associatedwith the first point of interest is greater than a first threshold. 16.The computing device of claim 15, wherein the processing unit is furtherconfigured to define a third subregion in the first subregion, whereinthe third subregion surrounds a third point of interest having a thirddensity of mobile devices greater than a third threshold but less thanthe first threshold.
 17. The computing device of claim 16, wherein theprocessing unit is further configured to determine whether to calculatethe usage of the cellular network in the third subregion based on apredefined frequency factor, a predefined time slot, and the number ofsubregions defined in the region.
 18. The computing device of claim 13,wherein the processing unit is further configured to reserve thebandwidth of the cellular network in the first subregion in the secondtime slot based on the forecasted usage amount of the cellular network.